ISSN: 1693-6930
TELKOMNIKA Vol. 14, No. 1, March 2016 : 254 – 261
256 The example of a microscopic image is shown in Figure 2. Before doing preprocess, the
microscopic image is cropping manually and then we get sub-image.
Figure 2. manual cropping a microscopic image b sub-image
2.2. Preprocessing
We conducted a series of preprocessing image to get the best segmentation image that will be extracted. The preprocessing stage can be seen in Figure 3. In this research, we used
150 sub-images from three pathogens. Sub-image should be converted to a grayscale image, then we used median smoothing. The smoothing filter is used for blurring and reducing noise.
The median filter is a commonly used nonlinear operator that replace is the original gray level of a pixel by the median of the gray levels in the pixels of specified neighborhood [19, 20]. This
filter is often useful because it can reduce noise without blurring edges in the image. Moreover, then we used Otsu thresholding, Otsu method is aimed at finding the optimal value for the
global threshold [21]. It is based on the interclass variance maximization [22, 23]. We applied region filling if the image has a hole so that it can be solved. We used median smoothing again
as removal of small details from an image prior to large object extraction, and bridging of small gaps in lines or curves [19, 21] and finally we used dilate operation.
Figure 3. Preprocessing a sub-image b grayscale c median smoothing d Otsu thresholding e fill hole f median smoothing g dilate operation
2.3. Feature Extraction
Features of an object are usually used to classify the object. The goal is to transform the images into data and then to extract information reflecting the visual pattern [16]. The
morphological features consist of basic features area, perimeter, convex area, convex perimeter and derivative features compactness, solidity, convexity and roundness. The
explanation of basic and derivative features are as follows:
The area is represented by the total number of non-zero pixels within the boundary [24]. Area of a binary region R can be found by simply counting the image pixels that make up the
region [22]. Perimeter or circumference of a region R is defined as the length of its outer contour,
where R must be connected [22]. The perimeter is calculated by measuring the sum of the distances between successive boundary pixels [24]. The simplest measure of the perimeter is
obtained by counting the number of boundary pixels that belong to an object [20].
TELKOMNIKA ISSN:
1693-6930
Morphological Feature Extraction of Jabon’s Leaf Seedling Pathogen using… Melly Br Bangun 257
The convex area is calculating the convex hull area in which the empty area between the convex hull boundary and the boundary object, loaded object and the pixel values that
included in the object area. The convex hull is the smallest polygon convex that contains all points of the region R
[22]. The convex perimeter is the circumference or limits on the convex hull. The illustration of basic features is shown in Figure 4.
Area Convex hull
Convex Area Convex perimeter
Perimeter 1
1 1
1 1
2 2
2 2
2 3
3 3
3 3
4 4
4 4
4 5
5 5
5 5
6 6
6 6
6 7
7 7
7 7
8 8
8 8
8 a
b c
d e
Figure 4. Basic features a area, b perimeter, c convex hull, d convex area, e convex perimeter
Compactness is the relation between a region’s area and its perimeter [22]. According to [16], compactness is defined as the ratio between the area of an object and the area of a
circle with the same perimeter. The maximum value of 1 to form a circle. Compactness calculation is defined in Equation 1 as below.
1 Roundness is the ratio of the area of an object to the area of a circle with the same
perimeter of the convex hull object [16]. Roundness calculation is defined in Equation 2.
_
2 Solidity is the ratio of the area of an object to the area of a convex hull of the object.
Solidity measures the density of an object [16]. Solidity calculation is defined in Equation 3.
_
3 Convexity is the relative amount that an object differs from a convex object, and this
value represents the ratio of the perimeter of an object’s convex hull to the perimeter of the object itself [16]. According to [25] the convexity is defined as the ratio of perimeters of the
convex hull with original contour. Convexity calculation is defined in Equation 4 as below:
_
4 The illustration of derivative features is shown in Figure 5.
Figure 5. Derivative features a compactness b roundness c solidity d convexity
ISSN: 1693-6930
TELKOMNIKA Vol. 14, No. 1, March 2016 : 254 – 261
258
2.4. Data Analysis
